{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-03-21T06:41:18.907208Z",
     "start_time": "2025-03-21T06:41:18.894019Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import gym\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "import rl_utils\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.distributions import Normal\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ch18.SAC_Continuous import *"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-21T06:43:15.993363Z",
     "start_time": "2025-03-21T06:41:19.544063Z"
    }
   },
   "cell_type": "code",
   "source": [
    "env_name = 'Pendulum-v0'\n",
    "env = gym.make(env_name)\n",
    "state_dim = env.observation_space.shape[0]\n",
    "action_dim = env.action_space.shape[0]\n",
    "action_bound = env.action_space.high[0]\n",
    "random.seed(0)\n",
    "env.seed(0)\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0)\n",
    "\n",
    "actor_lr = 3e-4\n",
    "critic_lr = 3e-3\n",
    "alpha_lr = 3e-4\n",
    "num_episodes = 100\n",
    "hidden_dim = 128\n",
    "gamma = 0.99\n",
    "tau = 0.005\n",
    "buffer_size = 100000\n",
    "minimal_size = 1000\n",
    "batch_size = 64\n",
    "target_entropy = -env.action_space.shape[0]\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "replay_buffer = rl_utils.ReplayBuffer(buffer_size)\n",
    "agent = SACContinuous(state_dim,hidden_dim, action_dim, action_bound, actor_lr, critic_lr, alpha_lr, target_entropy, tau, gamma, device)\n",
    "\n",
    "return_list = rl_utils.train_off_policy_agent(env,agent,num_episodes,replay_buffer,minimal_size,batch_size)\n"
   ],
   "id": "4355394672541dde",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|██████████| 10/10 [00:06<00:00,  1.58it/s, episode=10, return=-1532.511]\n",
      "Iteration 1: 100%|██████████| 10/10 [00:11<00:00,  1.20s/it, episode=20, return=-1073.622]\n",
      "Iteration 2: 100%|██████████| 10/10 [00:12<00:00,  1.23s/it, episode=30, return=-406.801]\n",
      "Iteration 3: 100%|██████████| 10/10 [00:12<00:00,  1.22s/it, episode=40, return=-286.934]\n",
      "Iteration 4: 100%|██████████| 10/10 [00:12<00:00,  1.22s/it, episode=50, return=-256.958]\n",
      "Iteration 5: 100%|██████████| 10/10 [00:12<00:00,  1.22s/it, episode=60, return=-199.417]\n",
      "Iteration 6: 100%|██████████| 10/10 [00:12<00:00,  1.22s/it, episode=70, return=-141.414]\n",
      "Iteration 7: 100%|██████████| 10/10 [00:12<00:00,  1.22s/it, episode=80, return=-109.022]\n",
      "Iteration 8: 100%|██████████| 10/10 [00:12<00:00,  1.23s/it, episode=90, return=-187.296]\n",
      "Iteration 9: 100%|██████████| 10/10 [00:12<00:00,  1.24s/it, episode=100, return=-150.165]\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-21T06:43:50.406921Z",
     "start_time": "2025-03-21T06:43:50.276474Z"
    }
   },
   "cell_type": "code",
   "source": [
    "episodes_list = list(range(len(return_list)))\n",
    "plt.plot(episodes_list,return_list)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('SAC on {}'.format(env_name))\n",
    "plt.show()"
   ],
   "id": "c70c1c9398fe3bd0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-21T06:43:54.466768Z",
     "start_time": "2025-03-21T06:43:54.435311Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class CQL:\n",
    "    '''CQL算法'''\n",
    "    def __init__(self,state_dim,hidden_dim,action_dim,action_bound,\n",
    "                 actor_lr,critic_lr,alpha_lr,\n",
    "                 target_entropy,tau,gamma,device,\n",
    "                 beta,num_random):\n",
    "        self.actor = PolicyNetContinuous(state_dim,hidden_dim,action_dim,action_bound).to(device)\n",
    "        self.critic_1 = QValueNetContinuous(state_dim,hidden_dim,action_dim).to(device)\n",
    "        self.critic_2 = QValueNetContinuous(state_dim,hidden_dim,action_dim).to(device)\n",
    "        self.target_critic_1 = QValueNetContinuous(state_dim,hidden_dim,action_dim).to(device)\n",
    "        self.target_critic_2 = QValueNetContinuous(state_dim,hidden_dim,action_dim).to(device)\n",
    "        self.target_critic_1.load_state_dict(self.critic_1.state_dict())\n",
    "        self.target_critic_2.load_state_dict(self.critic_2.state_dict())\n",
    "\n",
    "        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)\n",
    "        self.critic_1_optimizer = torch.optim.Adam(self.critic_1.parameters(), lr=critic_lr)\n",
    "        self.critic_2_optimizer = torch.optim.Adam(self.critic_2.parameters(), lr=critic_lr)\n",
    "        self.log_alpha = torch.tensor(np.log(0.01),dtype=torch.float)\n",
    "        self.log_alpha.requires_grad = True\n",
    "        self.log_alpha_optimizer = torch.optim.Adam([self.log_alpha], lr=alpha_lr)\n",
    "        self.target_entropy = target_entropy  # 目标熵大小\n",
    "        self.gamma = gamma\n",
    "        self.tau = tau\n",
    "\n",
    "        self.beta = beta  # CQL损失函数中的系数\n",
    "        self.num_random = num_random # CQL中的动作采样数\n",
    "\n",
    "    def take_action(self,state):\n",
    "        state = torch.tensor([state],dtype=torch.float).to(device)\n",
    "        action = self.actor(state)[0]\n",
    "        return [action.item()]\n",
    "\n",
    "    def soft_update(self,net,target_net):\n",
    "        for param_target,param in zip(target_net.parameters(),net.parameters()):\n",
    "            param_target.data.copy_(param_target.data * (1.0 - self.beta) + param.data * self.beta)\n",
    "\n",
    "    def update(self,transition_dict):\n",
    "        states = torch.tensor(transition_dict['states'],dtype=torch.float).to(device)\n",
    "        actions = torch.tensor(transition_dict['actions'],dtype=torch.float).view(-1,1).to(device)\n",
    "        rewards = torch.tensor(transition_dict['rewards'],dtype=torch.float).view(-1,1).to(device)\n",
    "        next_states = torch.tensor(transition_dict['next_states'],dtype=torch.float).to(device)\n",
    "        dones = torch.tensor(transition_dict['dones'],dtype=torch.float).view(-1,1).to(device)\n",
    "        rewards = (rewards+8.0)/8.0\n",
    "\n",
    "        next_actions,log_prob = self.actor(next_states)\n",
    "        entropy = -log_prob\n",
    "        q1_value = self.target_critic_1(next_states,next_actions)\n",
    "        q2_value = self.target_critic_2(next_states,next_actions)\n",
    "        next_value = torch.min(q1_value,q2_value) + self.log_alpha.exp() * entropy\n",
    "        td_target = rewards + self.gamma * next_value * (1 - dones)\n",
    "\n",
    "        critic_1_loss = torch.mean(F.mse_loss(self.critic_1(states,actions),td_target.detach()))\n",
    "        critic_2_loss = torch.mean(F.mse_loss(self.critic_2(states,actions),td_target.detach()))\n",
    "\n",
    "        # 以上与SAC相同，以下Q网络更新是CQL的额外部分\n",
    "        batch_size = states.shape[0]\n",
    "        random_unif_actions = torch.rand([batch_size * self.num_random,actions.shape[-1]],dtype=torch.float).uniform_(-1,1).to(device)\n",
    "        random_unif_log_pi = np.log(0.5 ** next_actions.shape[-1])\n",
    "        tmp_states = states.unsqueeze(1).repeat(1,self.num_random,1).view(-1,states.shape[-1])\n",
    "        tmp_next_states = next_states.unsqueeze(1).repeat(1,self.num_random,1).view(-1,next_states.shape[-1])\n",
    "        random_curr_actions, random_curr_log_pi = self.actor(tmp_states)\n",
    "        random_next_actions,random_next_log_pi = self.actor(tmp_next_states)\n",
    "        q1_unif = self.critic_1(tmp_states,random_unif_actions).view(-1,self.num_random,1)\n",
    "        q2_unif = self.critic_2(tmp_states,random_unif_actions).view(-1,self.num_random,1)\n",
    "        q1_curr = self.critic_1(tmp_states,random_curr_actions).view(-1,self.num_random,1)\n",
    "        q2_curr = self.critic_2(tmp_states,random_curr_actions).view(-1,self.num_random,1)\n",
    "        q1_next = self.critic_1(tmp_states,random_next_actions).view(-1,self.num_random,1)\n",
    "        q2_next = self.critic_2(tmp_states,random_next_actions).view(-1,self.num_random,1)\n",
    "        q1_cat = torch.cat([q1_unif - random_unif_log_pi,q1_curr - random_curr_log_pi.detach().view(-1,self.num_random,1),q1_next - random_next_log_pi.detach().view(-1,self.num_random,1)],dim=1)\n",
    "        q2_cat = torch.cat([q2_unif - random_unif_log_pi,q2_curr - random_curr_log_pi.detach().view(-1,self.num_random,1),q2_next - random_next_log_pi.detach().view(-1,self.num_random,1)],dim=1)\n",
    "\n",
    "        qf1_loss_1 = torch.logsumexp(q1_cat,dim=1).mean()\n",
    "        qf2_loss_1 = torch.logsumexp(q2_cat,dim=1).mean()\n",
    "        qf1_loss_2 = self.critic_1(states,actions).mean()\n",
    "        qf2_loss_2 = self.critic_2(states,actions).mean()\n",
    "        qf1_loss = critic_1_loss + self.beta * (qf1_loss_1 - qf1_loss_2)\n",
    "        qf2_loss = critic_2_loss + self.beta * (qf2_loss_1 - qf2_loss_2)\n",
    "\n",
    "        self.critic_1_optimizer.zero_grad()\n",
    "        qf1_loss.backward(retain_graph=True)\n",
    "        self.critic_1_optimizer.step()\n",
    "        self.critic_2_optimizer.zero_grad()\n",
    "        qf2_loss.backward(retain_graph=True)\n",
    "        self.critic_2_optimizer.step()\n",
    "\n",
    "        # 更新策略网络\n",
    "        new_actions,log_prob = self.actor(states)\n",
    "        entropy = -log_prob\n",
    "        q1_value = self.critic_1(states,new_actions)\n",
    "        q2_value = self.critic_2(states,new_actions)\n",
    "        actor_loss = torch.mean(-self.log_alpha.exp() * entropy - torch.min(q1_value,q2_value))\n",
    "        self.actor_optimizer.zero_grad()\n",
    "        actor_loss.backward()\n",
    "        self.actor_optimizer.step()\n",
    "\n",
    "        # 更新alpha值\n",
    "        alpha_loss = torch.mean((entropy - self.target_entropy).detach() * self.log_alpha.exp())\n",
    "        self.log_alpha_optimizer.zero_grad()\n",
    "        alpha_loss.backward()\n",
    "        self.log_alpha_optimizer.step()\n",
    "\n",
    "        self.soft_update(self.critic_1,self.target_critic_1)\n",
    "        self.soft_update(self.critic_2,self.target_critic_2)"
   ],
   "id": "590380573adb587b",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-21T06:53:39.559321Z",
     "start_time": "2025-03-21T06:43:55.818858Z"
    }
   },
   "cell_type": "code",
   "source": [
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "env.seed(0)\n",
    "torch.manual_seed(0)\n",
    "\n",
    "beta = 0.5\n",
    "num_random = 5\n",
    "num_epochs = 100\n",
    "num_trains_per_epoch = 500\n",
    "\n",
    "agent = CQL(state_dim,hidden_dim,action_dim,action_bound,actor_lr,critic_lr,alpha_lr,target_entropy,tau,gamma,device,beta,num_random)\n",
    "\n",
    "return_list = []\n",
    "for i in range(10):\n",
    "    with tqdm(total=int(num_epochs/10),desc='Iteration %d' % i) as pbar:\n",
    "        for i_epoch in range(int(num_epochs/10)):\n",
    "            # 此处与环境交互只是为了评估策略，最后作图用，不用于训练\n",
    "            epoch_return = 0\n",
    "            state = env.reset()\n",
    "            done = False\n",
    "            while not done:\n",
    "                action = agent.take_action(state)\n",
    "                next_state, reward, done, _ = env.step(action)\n",
    "                state = next_state\n",
    "                epoch_return += reward\n",
    "            return_list.append(epoch_return)\n",
    "\n",
    "            for _ in range(num_trains_per_epoch):\n",
    "                b_s,b_a,b_r,b_ns,b_d = replay_buffer.sample(batch_size)\n",
    "                transition_dict = {'states':b_s,'actions':b_a,'rewards':b_r,'next_states':b_ns,'dones':b_d}\n",
    "                agent.update(transition_dict)\n",
    "\n",
    "            if (i_epoch+1) % 10 ==0:\n",
    "                pbar.set_postfix({'epoch': '%d' % (num_epochs / 10 * i + i_epoch+1),'return': '%.3f' % np.mean(return_list[-10:])})\n",
    "            pbar.update(1)"
   ],
   "id": "a1128c83eb936b97",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|██████████| 10/10 [00:58<00:00,  5.80s/it, epoch=10, return=-1554.193]\n",
      "Iteration 1: 100%|██████████| 10/10 [00:58<00:00,  5.83s/it, epoch=20, return=-894.626]\n",
      "Iteration 2: 100%|██████████| 10/10 [00:58<00:00,  5.83s/it, epoch=30, return=-417.881]\n",
      "Iteration 3: 100%|██████████| 10/10 [00:58<00:00,  5.84s/it, epoch=40, return=-478.882]\n",
      "Iteration 4: 100%|██████████| 10/10 [00:58<00:00,  5.88s/it, epoch=50, return=-125.430]\n",
      "Iteration 5: 100%|██████████| 10/10 [00:58<00:00,  5.86s/it, epoch=60, return=-187.606]\n",
      "Iteration 6: 100%|██████████| 10/10 [00:58<00:00,  5.82s/it, epoch=70, return=-174.857]\n",
      "Iteration 7: 100%|██████████| 10/10 [00:58<00:00,  5.86s/it, epoch=80, return=-123.412]\n",
      "Iteration 8: 100%|██████████| 10/10 [00:58<00:00,  5.80s/it, epoch=90, return=-189.926]\n",
      "Iteration 9: 100%|██████████| 10/10 [00:58<00:00,  5.85s/it, epoch=100, return=-144.258]\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-21T06:55:11.412934Z",
     "start_time": "2025-03-21T06:55:11.149051Z"
    }
   },
   "cell_type": "code",
   "source": [
    "epochs_list = list(range(len(return_list)))\n",
    "plt.plot(epochs_list,return_list)\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('returns')\n",
    "plt.title('CQL on {}'.format(env_name))\n",
    "plt.show()\n",
    "\n",
    "mv_return = rl_utils.moving_average(return_list,9)\n",
    "plt.plot(epochs_list,mv_return)\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('returns')\n",
    "plt.title('CQL on {}'.format(env_name))\n",
    "plt.show()"
   ],
   "id": "df063affc3e48350",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "fc371f1217d267d0"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
